Active Management of Complex Non-self-finalising Behaviours through Deep Analytics

通过深度分析主动管理复杂的非自我完成行为

• 批准号: LP140100937
• 负责人: Prof Guandong Xu
• 金额: $ 25.38万
• 依托单位: University of Technology Sydney
• 依托单位国家: 澳大利亚
• 项目类别: Linkage Projects
• 财政年份: 2015
• 资助国家: 澳大利亚
• 起止时间: 2015-08-05 至 2018-08-04
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/LP140100937
• 关键词: Active; Management; Complex; Non; self

This project aims to build theoretical breakthroughs and novel tools for deep analytics and active management of non-self-finalising (NSF) individual and business behaviours, which are sophisticated and increasingly seen in public sectors such as taxation and business including banking and insurance. The challenging economic environment continues to make managing NSF behaviours difficult. To date, there are no sufficient theories or effective systems in data mining and behavioural science to systematically learn the intent, impact and patterns of NSF behaviours, and to suggest cost-effective responses to these behaviours. This project aims to ensure Australia’s leading role in innovation for evidence-driven enterprise behaviour analytics and management.

该项目旨在建立理论突破和新工具，用于深入分析和积极管理非自我终结（NSF）个人和企业行为，这些行为在税收和商业（包括银行和保险）等公共部门日益复杂。充满挑战的经济环境继续使管理NSF行为变得困难。到目前为止，在数据挖掘和行为科学方面还没有足够的理论或有效的系统来系统地学习NSF行为的意图，影响和模式，并对这些行为提出具有成本效益的反应。该项目旨在确保澳大利亚在以证据为导向的企业行为分析和管理创新方面发挥主导作用。